This document summarizes the results of new additions and edits to the initial analyses presented here. Notably, our analysis now includes Pink and Chum salmon stocks (N = 67, N = 48) from across the North Pacific Ocean, as well as newly added Sockeye stocks (N = 77).
We aim to characterize relationships between ocean conditions (SST and abundance of potential competitors) and salmon productivity (R/S) over space and time. There have been minor changes to our modelling approach since our last update.
We have compiled spawner-recruitment time series for all five species of Pacific salmon - available here. The analyses presented here include 192 of these populations of Sockeye, Pink, and Chum salmon originating from Washington to the Bering Sea.
Clicking on the legend items on the right hand side of the plot below will allow you to toggle between species.
Figure 1. Ocean entry locations of Sockeye (n=77), Pink (n=67), and Chum (n=48) salmon stocks included in analyses (total N=192).
Productivity (log[R/S]) time series of all stocks are shown below to illustrate the length and relative number of time series among regions and species. Vertical dashed lines represent breakpoints in ‘Era’ models and illustrate the coverage of data over periods of interest.
Figure 2. Productivity (log[R/S]) time series of Sockeye stocks (n=77) with vertical dashed lines indicating proposed ocean regime shifts.
Figure 3. Productivity (log[R/S]) time series of Pink stocks (n=67) with vertical dashed lines indicating proposed ocean regime shifts.
Figure 4. Productivity (log[R/S]) time series of Chum stocks (n=48) with vertical dashed lines indicating proposed ocean regime shifts.
We use 3(4) classes of generalized spawner-recruitment models:
Stationary models (e.g. Connors et al. 2020), which estimate time-invariant relationships. These have not changed other than the addition of new data.
‘Era’ models (e.g., Malick 2020), which allow relationships to vary among pre-defined periods that represent hypothesized shifts in NP Ocean processes and relationships. The 1976/77 ‘regime shift’ is no longer modelled following feedback from our last update, which brought to light that this shift from a cold to warm PDO phase is not a true ‘regime shift’. New iterations of these models consider a second potential regime shift at the onset of the ~2013 marine heatwave (‘the blob’). Currently, we consider brood years >= 2011 to have interacted with the marine heatwave regardless of the species and stock. In future analyses, the timing can be altered to better reflect the diverse life histories of populations in the analysis.
Random walk models (e.g., Malick 2020), which allow relationships to evolve gradually through time. These have not changed other than the addition of new data, however, a version that estimates ocean basin-scale trends in relationships is currently in development.
Hidden Markov models, which allow relationships to vary according to latent states, are not included in this update. They may be revisited in the future.
Details of each model class and our Bayesian model fitting procedure can be found here.
We continue to focus on:
The resolution of SST data is currently 2 x 2 degrees (ERSST). SST at ocean entry points are averaged spatially across 400 square km, and temporally across a 3-month period immediately following ocean entry for most stocks, to get a simple index of SST. Future analyses of SST may include:
( Brendan to fill in more about competitor analyses)
Figure 5. Posterior probability distributions of the predicted effect of SST (top), competitor abundance (middle), and the combined effect (bottom) on Sockeye productivity (R/S). Faint lines show stock-specific effects while bold lines show regional effects from hierarchical model. X-axis values represent the percent change in productivity per standard deviation unit increase in the covariate.
Figure 6. Posterior probability distributions of the predicted effect of SST (top), competitor abundance (middle), and the combined effect (bottom) on Pink salmon productivity (R/S). Faint lines show stock-specific effects while bold lines show regional effects from hierarchical model. X-axis values represent the percent change in productivity per standard deviation unit increase in the covariate.
Figure 7. Posterior probability distributions of the predicted effect of SST (top), competitor abundance (middle), and the combined effect (bottom) on Chum salmon productivity (R/S). Faint lines show stock-specific effects while bold lines show regional effects from hierarchical model. X-axis values represent the percent change in productivity per standard deviation unit increase in the covariate.
Figure 8. Posterior probability distributions of the predicted effect of SST and competitors on Sockeye productivity over three pre-defined time periods/eras (earliest in top panel). Regional mean effects are shown by bold lines and individual stocks’ distributions by light lines.
Figure 9. Posterior probability distributions of the predicted effect of SST and competitors on Pink salmon productivity over three pre-defined time periods/eras (earliest in top panel). Regional mean effects are shown by bold lines and individual stocks’ distributions by light lines. Note that data from Alaska for the most recent time period are needed (Figure 3).
Figure 10. Posterior probability distributions of the predicted effect of SST and competitors on Chum salmon productivity over three pre-defined time periods/eras (earliest in top panel). Regional mean effects are shown by bold lines and individual stocks’ distributions by light lines. Note that data from Alaska for the most recent time period are needed, and data in the Southeast Alaska region are sparse across all time periods (Figure 4).
Figure 11. Time-varying posterior mean estimates of SST and Competitor covariate effects on Sockeye productivity, modelled as a random walk. Individual stock estimates are in faint lines, while regional means and 80% CI are represented by bold lines and shaded areas. Region-wide means and CI are post-hoc calculations, rather than resulting from hierarchical model structures as in the stationary and era models.
Figure 12. Time-varying posterior mean estimates of SST and Competitor covariate effects on Pink salmon productivity, modelled as a random walk. Individual stock estimates are in faint lines, while regional means and 80% CI are represented by bold lines and shaded areas. Solid bold lines represent odd-year Pink stocks, while dashed lines are even-year stocks. Region-wide means and CI are post-hoc calculations, rather than resulting from hierarchical model structures as in the stationary and era models.
Figure 13. Time-varying posterior mean estimates of SST and Competitor covariate effects on Chum salmon productivity, modelled as a random walk. Individual stock estimates are in faint lines, while regional means and 80% CI are represented by bold lines and shaded areas. Region-wide means and CI are post-hoc calculations, rather than resulting from hierarchical model structures as in the stationary and era models.
Not finished, could use feedback! **********
Inference - We see evidence of nonstationarity across all 3 species - The degree of change varies among regions and species - There is some evidence that the ‘marine heatwave’ period changed ocean condition-productivity relationships
Next steps - Obtain updated data for Alaskan Pink and Chum stocks - Carry out sensitivity analyses for covariates as described - Make era model breaks consistent with each species’ life history - Continue work on modelling regional effects as a random walk